The present disclosure relates in general to systems and methods for object guidance and collision avoidance, particularly crane guidance in a factory or large building environment.
Cranes, forklift trucks, automated equipment, robots, and other hazards within a building, such as a factory, can have a direct impact on the health and safety of individuals within the building. Environments with a high-density of large parts, tooling, and assembly equipment pose a particularly difficult safety challenge because of the associated momentum of the larger objects, which can lead to potential near misses with individuals or other objects in the building, and in some cases, could result in impact with the individuals or other objects.
For example, in factories, such as for the manufacture of aircraft, an overhead crane move is planned on a case by case basis due to the dynamic location of equipment and people on the floor. As the crane moves to a location to pick up an item, the crane may come in contact with a number of items that can create a potential collision. In these settings, a significant amount of man power is needed to spot and guide the crane to and from certain locations, shutting down areas that may not need to be shut down or shutting down an area for longer than necessary.
Moreover, human reaction time is limited by the human speed to process the warnings and complexity of things known and things that can be seen and heard. This process is challenging, for example, in a factory environment due to noise reduction devices (e.g., ear plugs, music headphones, etc.) and also limited by line-of-sight threats.
Thus, individuals who work in manufacturing facilities, industrial yards, warehouses, and outdoor storage facilities in the proximity of industrial equipment are challenged with daily safety concerns. It would be desirable to provide efficient guidance when moving objects and to identify and warn individuals of potential conditions that could result in impact of the individual with the moving objects.